Mini-Masterclass Episode 9: Bullshit Bingo or Industry Savior?

Operative Intelligence: Bullshit Bingo or Industry Savior?

'Operative Intelligence' initially sounds like one of those terms that look good on every digitization slide but change little in everyday work.

So is it just bullshit bingo?

No. Behind the term lies a very specific challenge for our industry: In many companies, the experiential knowledge of employees and the available digital data are completely separate.

On one side are technicians who have known their machines for years. On the other side are machine, sensor, and ERP data in various systems. Both worlds contain valuable information. But as long as they are not connected, employees cannot leverage their combined potential.

Operational intelligence means breaking down exactly this wall.

Two worlds exist in your company

The first world consists of the knowledge and experience of your employees.

An experienced maintenance worker knows which noises are unusual. He remembers past disruptions, knows the peculiarities of a system, and can interpret technical relationships that cannot be deduced from a single measurement.

This knowledge is particularly valuable because it comes directly from everyday work. However, it is often tied to individual people and only available when the right colleague is present.

The second world consists of the digital data of your company.

This includes, for example:

• Machine and sensor data,
• Messages and feedback,
• Orders from the ERP system,
• Technical documentation,
• Maintenance information,
• as well as the previous system history.

These data can provide important clues. But they do not explain themselves.

A temperature value shows that something has changed. But it does not automatically explain why the temperature is rising, whether this behavior has occurred before, or what measure helped at that time.

Data without experience remain incomplete

Many companies invest in sensors, ERP systems, and digital platforms. This theoretically makes more and more data available.

In practice, however, the technician at the machine often cannot access this information. Perhaps he lacks access rights. Maybe he does not know the right system. Perhaps he would have to search through multiple applications or interpret complex data views.

Then the data is there, but it does not help solve the specific problem.

Conversely, the technician's experiential knowledge also remains limited if important data is missing. He can observe a machine and draw conclusions from his experience. But without historical measurements, past events, or current order information, he lacks part of the overall picture.

Both sides are valuable.

Only their connection creates operational intelligence.

What operational intelligence really means

Operational intelligence does not mean introducing as many dashboards as possible or providing every employee with additional key figures.

It's about providing each employee with exactly the information they need in a specific situation.

A technician stands in front of a failed system. At this moment, he does not need all the company's data. He needs the relevant information about this asset:

• What has happened currently?
• Which measurements have changed?
• Has this error pattern occurred before?
• What repairs have been carried out?
• What do experienced colleagues know about this system?
• What are the next sensible inspection steps?

Operational intelligence arises when this information becomes available without a long search and can be viewed together.

The employee no longer has to switch between isolated systems and personal knowledge sources. He receives the context he needs to solve the problem.

The wall between IT and workshop

In many companies, IT systems map processes from the organization's perspective. However, the technician's everyday work follows a different logic.

The ERP system thinks in terms of orders, numbers, fields, and status values. The employee at the machine thinks in terms of symptoms, causes, and technical relationships.

Both perspectives are justified. It becomes problematic when there is no understandable connection between them.

Then typical situations arise:

The technician sees a problem but cannot find the corresponding data. The database contains a malfunction but no understandable explanation. An experienced employee knows the solution, but his knowledge was never linked to the system history.

The information lies side by side instead of working together.

Operational intelligence overcomes this separation. It makes data accessible for operational work and connects it with the knowledge of the people who can interpret it.

Technology is just the connecting element

Technology alone does not create operational intelligence.

Another system, a new database, or an additional dashboard does not automatically solve the fundamental problem. What matters is whether the solution connects knowledge and data in a way that employees can actually work with.

Technology plays a supportive role in this.

It can merge information from different sources, assign content to a system, and make relevant connections visible. Artificial intelligence can additionally help understand natural language, structure large amounts of information, and simplify access.

The technician does not have to become a data analyst. He describes his problem or asks a specific question. The system helps him find the appropriate information.

Technology does not replace his experience. It expands his options for action.

The goal: Information at the moment of decision

In maintenance, it is not only important whether information is generally available. What matters is whether it is available at the right moment.

If an employee has to search through multiple systems, call colleagues, or request documents, avoidable waiting time arises. During a system downtime, this time can directly impact production.

Operational intelligence therefore pursues a clear goal:

Every employee should be able to access the knowledge and data they need to solve their current problem at any time.

Not more information at any cost.

But the right information in the right context.

Knowledge and data must grow together

Operational intelligence is not a one-time digitization project. The shared information base evolves with each event.

A technician handles a malfunction and documents his observations. The machine data shows the technical course. The ERP system contains the order and the materials used. The feedback describes the found cause and the solution implemented.

When this information is connected, a more complete picture of the system emerges.

The next time a similar error occurs, the team does not start from scratch. They can rely on the existing data and experiences.

This way, every resolved case becomes a learning opportunity for the entire organization.

The key insights of the episode

The central message is: **Operational intelligence connects the knowledge of people with the data of systems.**

The most important key takeaways:

• In many companies, two separate worlds exist. On one side is the experiential knowledge of employees, on the other are machine and ERP data.
• Data do not explain themselves. Only people can place measurements and events in the technical context.
• Experiential knowledge alone is also not enough. For informed decisions, technicians need access to current and historical data.
• Access is often the biggest hurdle. Information is available but not immediately usable for the employee at the machine.
• Operational intelligence breaks down this wall. It connects knowledge and data in the context of the respective problem.
• Technology serves as a translator. It brings information together and makes it available without complicated system searches.
• The goal is actionability. Every employee should receive exactly the information they need to solve their current task.

Operational intelligence is therefore not a new buzzword for old dashboards.

It describes an industry where employees are no longer separated from important data and valuable experiential knowledge no longer disappears in individual minds.

An industry where people, knowledge, and technology work together to solve problems.

And that is precisely where its future viability lies.

• Make knowledge and data silos visible
• Directly connect information with systems and problems
• Provide employees with the appropriate context at the push of a button